Nishant Khatri

Love this - AI is the Oppenheimer moment for marketing. It fundamentally collapses the time and cost of production. A $2.5 million campaign that used to take four months can now be delivered for $500,000 in four weeks. Legacy firms that charge based on hours with the 'time-driven' model are existentially threatened by this.

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Checked out Forerunner/Kirsten Green's "The Architects of Experience" piece (linked in comments). Sharing some thoughts below. Every 10-year platform shift resets consumer expectations (Web → Mobile → AI). Kirsten says the AI era belongs to products that feel effortless: proactive, predictive, always learning. It’s not about adding an LLM garnish; it’s about architecting the entire business around a compounding data loop. So, why does it matter? Moat ≠ code → The defensible layer is a proprietary data flywheel that learns faster than users can churn. Talent remix → Winning teams look less like pure engineers, more like “orchestrators” who weave off the shelf AI, data strategy, and distribution. Trust is the gate → Human in loop design and transparent governance are table-stakes for adoption, and eventual regulation. Questions to consider for consumer AI founders... 1. Moat depth: What makes your data flywheel unique, and how quickly does it compound vs. the big LLM platforms? 2. Distribution edge: When AI tooling is commoditized, what non-technical channel (community, brand, regulated access) gets you in front of users first? 3. Human-machine balance: Where does your product deliberately stop automating to preserve agency and delight? 4. Regulatory resilience: How will privacy portability rules affect CAC, margins, or data lock in? My counterview: Total automation can flatten delight. Some categories thrive on intentional friction (e.g. luxury, craft, serendipity). Winners will know when to keep a human in the loop.

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I rarely make predictions, but... No company founded after 2026 will organically grow to more than 10k FTEs. The leverage of rapid iterative decision loops by teams using best-in-class AI has reached escape velocity over traditional economies of scale.

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"This space moves so fast that every quarter, everything you do can be obliterated," said Glean founder Arvind Jain. "We had to shift to a mindset of constantly rebuilding. Your moat is not your tech but the value you provide to your customer, those deep relationships you build, and the team that’s with you to build and execute in that kind of environment." An intimate, thoughtful gathering at last night’s AI Pioneers Summit by Decibel Partners and Latent Space Podcast with speakers that included (photo, left to right) Eric Simons (bolt.new), Behnam Neyshabur(Anthropic), swyx ​(inc a celebration of 10M downloads), Arvind Jain (Glean), and Laura Burkhauser (Descript). Simons: “We’re seeing more developer-adjacent product people building direct experiences.. that will lead to developers doing deeper thinking & reviewing PM work, as PMs also go deeper to shape the front-end.” Burkhauser spoke to the complexity of AI video editing, where context goes far beyond the script to understand how humans see, feel and sense, and uncover the reasoning behind what a viewer expects. Neyshabur's pursuit of creating an AI scientist is rooted in his curiosity about how a model learns from human interaction and the nature of learning itself. Our breakout session was insightful, inclusive, and energetic. Good to spend time with founders like Bella Liu (Orby AI - A Uniphore Company) and catch up with Ajay Singh. A great mix of technical depth and openness for conversations that stay with you.

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Here is a framework for nations to work with revolutions such as #AI and #Quantum. First you have your people learn, then dabble and become early/late adopters (hopefully not laggards!), then become effective technical users and finally value creators. It is only then that the economy benefits from your investment, and you create sustainable job growth. While #India is a key market for #Quantum with millions thirsting to learn and actually do things with this technology, there are still large gaps between: 1. Where Quantum Computing is today Vs where it needs to be for mass adoption [Quantum Sensing on the other hand is ready for prime time and GPS compromising incidents are only increasing] 2. The specialist work being done Vs the ability to hire millions of generalists 3. What it costs to tap into the technology Vs perception of what it should cost as a commodity 4. The talent it takes to produce world class indigenous technology Vs the talent that actually exists on the ground 5... Each of these gaps will close over time. In the lead-up and as a first step, India first needs to learn enough about this technology, ideally from the very best, and spread awareness. The starting point of this journey should ideally not be from dull videos or low quality social media content, but from hands-on practical learning that is delivered through high quality polished content. Such practical training effectively complements all the theoretical content you learn in universities. Q-CTRL's Black Opal product is the world's leading interactive learning tool that gives you just enough fundamentals (think 101) required to program a quantum computer. With over 28,000 users worldwide and success across the UK (Tamil Nadu adopted it last year as part of the naanmudhalvan program). And now, we have expanded our presence in India through a network of local resellers. We encourage Indian companies, governments and universities to work with us either directly or through our contacts. https://lnkd.in/gjJWHYB3 We also look forward to aligning with AICTE standards and to make our content even more relevant to India. Waseem Shiraz, Shobhit Gupta, Nagendra Nagaraja, Reena Dayal, Anil Prabhakar, Kamakoti Veezhinathan, Ajai Chowdhry, Harshan Budke, Sunil Gupta, Arindam Ghosh, Ravi Puvvala Dr M Jayaprakasan, ISDS., National Skill Development Corporation, MindTech, Kquanta Research, RV College Of Engineering, Sanjay Chittore, Uttkrist Innovations Private Limited, Subu Gupta, AICTE QRDLab - Quantum Computing in India, IBM DRDO, Ministry of Defence, Govt. of India, Ashutosh Sharma, Urbasi Sinha, Bloq Quantum LTIMindtree, Tata Consultancy Services Abhay Karandikar, NPTEL, National Quantum Mission, Eltech, Andhra Pradesh Information Technology Academy ITE and C Department Government of AP, Jayesh Ranjan, CDAC Bangalore, Kaushalya The skill University

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This Scale AI story might matter more than you think. Meta just invested $14.3B to take a 49% stake in Scale AI — and brought Scale’s CEO into its internal “superintelligence” unit. On the surface, it looks like a major bet on data. But look a little deeper, and it’s a shift in how control is consolidating across the AI value chain. Why it matters: – Scale was one of the few trusted, neutral data providers for labeling and evaluation – Now that neutrality is in question, contracts are reportedly getting pulled by OpenAI and Google – Startups and enterprises alike are suddenly reconsidering where their training signal comes from What comes next: – A surge of boutique data providers — decentralized, domain-specific, and more transparent – Startups reframing data control as part of their moat: “We don’t just use models — we own the context” – Infra teams re-evaluating dependencies — either proactively, or reactively when vendor dynamics shift As training data usage becomes more regulated and controlled — moving away from the wild west of early foundation model training — the pressure to secure differentiated data pipelines only increases. When we talk about defensibility in AI, it’s tempting to focus on model IP or feature differentiation. But data leverage — how it’s sourced, labeled, evaluated, and governed — is becoming a more important part of the strategic equation. It’s not quite infrastructure. But it’s no longer a commodity either. It’s a layer of increasing consequence — and one worth watching closely. Are you rethinking your data leverage yet? https://lnkd.in/gx9GPefp

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From financial services and housing to groceries and healthcare, the 50+ consumer market is unmatched in size and buying power. Learn from thought leaders from Morgan Stanley, Sloan Kettering, and more on how to tap into the largest - and fastest growing - market in the world. 💰 🤖 🌎 Forbes Technology Council Forbes Councils HBS Healthcare Alumni Association a2 Collective | a2PilotAwards.ai National Institute on Aging (NIA) CAREGIVERZONE.COM Electronic Caregiver AWS for Healthcare & Life Sciences Intel Corporation

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Had such a great chat with Vivien Lin! She brought so much wisdom and vivid insights to the conversation: 1. We're trying to work with our users, when they make money we make money. We try not to stand on the opposite. 2. From the start we decided to exit China market. We never touched US users. It's all because we are quite self-disciplined and self-regulated. 3. We consider whether this product or whether a feature can truly solve the problem for the user. 4. From 2018, 2019 is always put our top priority to think about how we bring more new user to the crypto industry. It's not like competing the existing user with other exchange. 5. Ideally, the final product should be you don't need to worry about what functions sit in what page. You just go to the AI or the AI assistant to tell him what you want. 6. The difference in the roadmap between crypto and traditional finance is rooted in how the industry got originated. For traditional finance, banking and payment that's naturally where finance comes from than lending than trading. At crypto, the very first need where the industry originated is mining. 7. I think, we are still in a very precious time window especially for exchange it to become a sort of mega platform in near future. 8. My suggestion is always be open minded - open minded to the technology, business model and even all those new ideas. 9. My suggestion to everyone, even if you are a legal, you are a product manager or you are in the operation team, is to study programming, learn how to code. 10. The slides always tell the good stories but you need to get down to the details to understand what's happening. 11. Before all those regulations put into place there has to be someone familiar with how possible the regulation is going to be implemented in the future and we can get prepared beforehand. 12. Without one major hack exchange cannot be a mature exchange. 13. If I know what they are doing I can read their code, I can get a very direct sense about the quality of the project instead of really looking at the powerpoint. Watch the full session here: https://lnkd.in/dGhbXdwr BingX

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I had a fantastic time talking with Kishore Kothandaraman on CMO Diaries about what it takes to scale content and build trust in the age of AI. Here are three takeaways from our conversation: 1️⃣ Content is still the engine that powers great marketing. But what’s changing is how it’s being built and shipped. The real value of AI isn’t just faster writing. It’s revamping the entire content supply chain — from brief to personalized, localized asset — in order to uplevel the quality of your marketing at large. 2️⃣ Relationship capital is the new ROI. When AI takes care of the repetitive work, marketers get time back to focus on what actually builds trust and connection. Customer advisory boards, in-person events, and shared experiences all matter more now. 3️⃣ Throw out your existing playbooks. That winning strategy you’ve used and reused at your last several companies? There’s a good chance it’s irrelevant now. The teams pulling ahead are rethinking entire strategies and systems with an AI-first lens. In Jasper’s State of AI in Marketing report, 78% of marketers said AI has already improved their job satisfaction. That’s a hopeful signal, but only if we’re intentional about how we use it to scale the right things. Is there a way AI has improved your job satisfaction? ▶️ Watch the full conversation:

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After nearly 17 years at LinkedIn, I’m beginning a new chapter: co‑founding KwantumLabs.ai with Marco Vriens to reimagine brand health tracking and product–market fit testing with AI. In 2008, I joined a 350‑person startup with bold ambitions. What followed was the ride of a lifetime: scaling teams, building measurement systems that reshaped how we understand brand and customer value, and helping turn entrepreneurial dreams into durable, large‑scale impact. The experience cemented my belief that great companies live at the intersection of vision, data, and execution. The greatest privilege has been the people. Thank you to Daniel Shapero for taking a bet on me, and to mentors, managers and partners including James Raybould, Joanna Pomykala, Sunil Manhapra, Sanjeev Kapur, Minjae Ormes, Mark Lobosco, Craig Mackintosh, Ryan Milhous, and Keith Pradhan — as well as many others who influenced and inspired me along the way. And to every teammate I had the honor of working with or leading: thank you for your trust, collaboration, and for shaping the leader I’ve become. Now, I’m thrilled to help drive KwantumLabs.ai forward with my long‑time friend and mentor, Marco V. Our mission is to reimagine how companies track brand health and validate product–market fit. Why AI? Because it unlocks the next curve of decision quality — synthesizing signals at scale, reducing noise and bias, and turning weeks of analysis into hours, without sacrificing rigor. Faster, smarter, more affordable insight so leaders can move with confidence. We’re starting with two solutions: -BrandSight — quantifies how brands live in people’s minds and links associations to business value. -LaunchFit — rapidly tests and refines product–market fit to de‑risk positioning, pricing, and go‑to‑market. In parallel, I’ll continue advising startups and mentoring founders — helping turn zero‑to‑one sparks into enduring businesses. Grateful for the past. Energized for what’s next. I also recognize these are challenging times across tech, with many friends and colleagues affected by layoffs. I’m optimistic that AI is opening new paths to build, learn, and create — and if I can be helpful with perspective or an introduction as you navigate what’s next, my inbox is open.

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Most people are building one powerful AI. At Brahma, we’re building teams of small agents that negotiate to solve real problems — pricing, media, supply chain. Simple idea. 18 months to make it work. No hype. Just building what works. #AgenticAI #AIAgents #BuildingInPublic

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Agentic commerce is about to fundamentally disrupt how people discover products. There is an opportunity in data infrastructure to build something absolutely monumental. Investing in Ankur Modi and David Mataciunas to go after this was an easy decision for me, for two reasons: 1. Ankur is one of the most persistent, intense and fast-learning founders I’ve ever worked with 2. Their founder-market fit is unbeatable. Ankur built a commerce platform which IPO’d, he led oversight platforms at Meta, and was part of the commerce experience leadership team at Amazon. David is a NeurIPS-published researcher with experience at IBM and Cohere, whose academic work with MIT is at the bleeding edge of LLM research Ankur and David have built an infra platform that might fundamentally change commerce. In weeks, they've signed brands doing hundreds of $m in GMV. They’ll be talking in more detail about their plans today, at the Entrepreneurs First demo day in SF.

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While adtech is an ecosystem the reality is publishers and intermediaries are a commodity. The only thing finite is ad budgets. In the long run the buy side always wins.

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New pod alert - Naveen Mohan leads product and monetisation at Otter, bringing deep experience from Adobe and Loom. In this conversation, Naveen and I discuss: -What are the leading indicators that your pricing is falling behind product value? -How do you structure soft versus hard paywalls at Otter to balance conversion with retention? -When does monetisation need to live in product versus finance versus growth? (Link in comments) Enjoy! Sponsored by Flexprice

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❗(...) One former Google engineer, Abhishek Iyer, created dummy web pages that appeared only in Google’s index. When he later asked ChatGPT about them, the bot responded with information pulled from those very pages, suggesting that Google search snippets are indeed being repurposed in ChatGPT responses (like Meta, Apple and Perplexity). 🟥 [Comment] The work of Abhishek Iyer, and the examples provided by Backlinko, represent a gateway to exploiting "LLM Grooming" (flooding the internet with SEO-optimized content, ensuring its inclusion in the training datasets and search indexes used by artificial intelligence). To this end, by creating content structured to be indexed by Google's search engine, this technique can be used to alter the knowledge of generative artificial intelligence (data poisoning) with the aim of generating misinformation in its responses to specific questions, as was the case in the context of the Russian Pravda operation, identified by VIGINUM (more than three million articles intended to seed the knowledge of the main AGIs on the market had been produced), which raises a critical point about the vulnerabilities of AI systems to the manipulation of their information sources. ▶️ Abhishek Iyer: How does ChatGPT Search the Web: Data-Driven Investigation https://lnkd.in/ePbk7gH6 ▶️ Backlinko: ChatGPT Is Using Google Search – We Tested It https://lnkd.in/ePbk7gH6 ▶️ Exposing Pravda: How pro-Kremlin forces are poisoning AI models and rewriting Wikipedia https://lnkd.in/e-yNrGyX

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From Measurement to Orchestration, with Pranav Piyush from Paramark.com In this week's Intelligent Marketer ep, Rishabh and I dug into the evolution of marketing measurement in a world where direct, click-based attribution has broken. The smartest marketing teams are building systems that triangulate incrementality testing + MMM results. Pranav discusses how getting this "central brain" right is a critical foundation for AI-driven orchestration & execution of an end-to-end marketing roadmap. Topics covered: - Policies, not dashboards: how to decode decisions as machine-readable rules that turn insights into actions - Confidence-weighted budgets: how to use error bars in lift/MMM results to drive budget reallocation - Latency-aware orchestration: how to incorporate daily platform signals vs weekly incrementality results vs quarterly MMM output - Creative as a portfolio: how to weight hit rate & EV alongside typical ROAS metrics - Constraint-aware bidding: how to bake margin, inventory, compliance, and brand safety costs into the decision function

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The Seismic Shift in Product Building Driven by AI Super interview of legendary operator & prolific investor Gokul Rajaram (Google, FB, Square) by Patrick O'Shaughnessy (Positive Sum) on the new operating manual- 4 tectonic shifts defining this new reality: I. The Shift in Definition: From "Wrapper" to "System of Record" The era of the "System of Action" (workflow) is over * The "CIO Threat": You're not competing with other startups but with your customer's IT depts. With low-code tools, CIOs are building internal agents to replace external "wrappers" for free * The Middleman Trap: Building on top of Salesforce/Slack/etc. is now dangerous. As incumbents cut off APIs to hoard data, you must own the System of Record (DB) to survive * The "Migration Moat": A better AI interface is insufficient to displace a legacy ERP. Only real moat is the unsexy build of "migration engines" that move data painlessly II. The Shift in Economics: From "Seats" to "Outcomes" The unit economics of software are decoupling from human headcount * Pricing Crisis: If one AI agent executes the workload of 5 humans, traditional per-seat pricing collapses. Shift to Outcome-Based Pricing (charging per resolution, per outcome) * "Agent Economy": moving toward a world where bots make purchases for humans. Can't show a banner ad to a bot; need to become the trusted API provider the agent is hard-coded to reference * "Hold-Out" Rule: For utility apps, ads destroy retention. Maintain a permanent "Hold-Out Group" (users who never see ads) to rigorously measure the true cost of monetization against your "Engagement Budget" III. The Shift in Engineering: From "Deterministic" to "Probabilistic" The constraint in building products is no longer production but selection. * Designing Evals > Writing Tests: Old software: Input A=Output B. AI software: Input A=Maybe Output B. Core engineering competency is shifting from bug detection to "Designing Evals" - systems that judge quality & "vibes" at scale * Judgment as the Scarcest Asset: With AI capable of generating infinite code (AI Slop), Taste is now a hard engineering skill * Real World Assets: With half-life of features collapsing, durability now comes from assets AI cannot code: regulatory licenses, proprietary hardware & exclusive supply IV. The Shift in Talent: From "Architect" to "Editor" The org pyramid is flattening. * "Prototyping" Mandate: No longer hire PMs who only write specs. Candidates must pass a "Prototyping Interview" - use AI tools to build working software live * Span of Control: The middle manager (3-4 people) is extinct. New structure is binary: Builders (ICs) or Orchestrators (managing 15+ agents/people) The Bottom Line: The last decade was defined by distribution & access. The next decade will be defined by judgment & outcomes. If your org is still hiring for specs, pricing for seats & building wrappers - you're running a legacy operating model. https://lnkd.in/g3jRTA8G #ai #artificialIntelligence

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I loved the way Shashank Panchangam (CPO at Crowley) articulated the future of product organizations: "Every team will be a hybrid team. Humans and AI agents working together." His vision is simple but powerful. PMs are great at identifying customer problems, developing unique solutions, and handling product strategy. AI agents handle the mundane work. Problem identification, value assessment, customer insights - that's human work. Requirements writing, documentation, manual processes - that's AI work. This is Human-AI optimization in action - and it's exactly what the best product teams are already building toward. Thanks Shashank for this brilliant insight on where we're headed!

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How do you turn AI uncertainty into ROI? Last quarter, a mid-sized NYC tech firm faced stalled innovation and decision fatigue. Their leadership team was overwhelmed by conflicting AI automation options. We stepped in with a cognitive audit, mapped their bottlenecks, and designed a private AI agent system tailored to their workflows. Within weeks, project delivery times dropped by 27%, and decision cycles became days, not weeks. Curious how a cognitive science-led approach can unlock your team’s next breakthrough? Let’s talk about your challenges. (If you’re navigating complex tech decisions, I’d welcome a direct message or connection.)

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